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Type: Journal Article
Author(s): Marj Tonini; Mário G. Pereira; Paolo Fiorucci
Publication Date: 2022

Estimating the probability of wildfire occurrence in certain areas, under particular environmental and anthropogenic conditions, is a powerful tool to support forest protection and management plans. In this context, the implementation of Wildfire Susceptibility Maps (WSM) and the investigation of the main driving factors (e.g., land cover class, type of vegetation, topography) are fundamental.

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Citation: Tonini, Marj; Pereira, Mario G.; Fiorucci, Paolo. 2022. Performance and efficiency of machine learning based approaches for wildfire susceptibility mapping. Environmental Sciences Proceedings 17(1):38.

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Topics:
Regions:
Keywords:
  • Bolivia
  • fire susceptibility
  • GIS - geographic information system
  • Italy
  • land cover
  • machine learning
  • model validation
  • Portugal
  • wildfires
Record Last Modified:
Record Maintained By: FRAMES Staff (https://www.frames.gov/contact)
FRAMES Record Number: 67067